{"id":"W4387122679","doi":"10.18357/anthropologica65120232598","title":"Taxis vs. Uber: Courts, Markets, and Technology in Buenos Aires, by Juan Manuel del Nido","year":2023,"lang":"en","type":"article","venue":"Anthropologica","topic":"Diverse multidisciplinary academic research","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Taxis; Humanities; Political science; Art; Sociology; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts","insufficient_payload"],"consensus_categories":["sts"],"category_scores_codex":[0.001106131,0.0001521545,0.0002652122,0.000463324,0.001541027,0.000077788,0.0006183393,0.0004708792,0.001028765],"category_scores_gemma":[0.0005922051,0.0001423934,0.00003398236,0.001373748,0.004705749,0.0002097422,0.0006197226,0.0005107753,0.000354058],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001116492,"about_ca_system_score_gemma":0.0001118093,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007417497,"about_ca_topic_score_gemma":0.001039081,"domain_scores_codex":[0.9976766,0.0003071685,0.0002459348,0.0005283444,0.0004114925,0.0008304448],"domain_scores_gemma":[0.9991844,0.0003124594,0.00007295054,0.0002410976,0.0000443579,0.0001447313],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001313868,0.0002276228,0.2329467,0.00003348285,0.00003787314,0.0005180315,0.005117828,0.000001839844,0.001311978,0.04259181,0.6664995,0.050582],"study_design_scores_gemma":[0.0008678127,0.0003037468,0.04550253,0.00005488049,0.00001229976,0.000009519527,0.0814047,0.0002757802,0.0004335875,0.02873546,0.8419028,0.0004969116],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8188414,0.001588955,0.00001101735,0.1547034,0.0003029146,0.0005947306,0.00006749776,0.0005028373,0.02338725],"genre_scores_gemma":[0.9498675,0.03951373,0.0001437814,0.0001164189,0.00005453148,0.00005790424,0.00001921837,0.00001537725,0.01021146],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1874441,"threshold_uncertainty_score":0.9998844,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02914517213196028,"score_gpt":0.3718766968052978,"score_spread":0.3427315246733376,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}